Objective-Driven AI: towards AI systems that can learn, remember, reason, and plan
Keynote
AI Technology
2024-02-08 | 10:50 AM - 11:15 AM | Tech & Strategy Stage
Information
- How could machines learn as efficiently as humans and animals?
- How could machines learn how the world works and acquire common sense?
- How could machines learn to reason and plan?
- Current AI architectures, such as Auto-Regressive Large Language Models fall short. I will propose a modular cognitive architecture that may constitute a path towards answering these questions.
- The centerpiece of the architecture is a predictive world model that allows the system to predict the consequences of its actions and to plan a sequence of actions that optimize a set of objectives.
- The objectives include guardrails that guarantee the system's controllability and safety. The world model employs a Hierarchical Joint Embedding Predictive Architecture (H-JEPA) trained with self-supervised learning.
- The JEPA learns abstract representations of the percepts that are simultaneously maximally informative and maximally predictable.
- The corresponding working paper is available here: https://openreview.net/forum?id=BZ5a1r-kVsf
